Waste biomass coming from a local coffee company, which supplied burnt ground coffee after an incorrect roasting process, was employed as a starting material in the composting plant of the Experimental Station of the University of Naples Federico II at Castel Volturno (CE). The direct molecular characterization of compost using ¹³C-NMR spectra, which was acquired through cross-polarization magic-angle spinning, showed a hydrophobicity index of 2.7% and an alkyl/hydroxyalkyl index of 0.7%. Compost samples that were collected during the early “active thermophilic phase” (when the composting temperature was 63 °C) were analyzed for the prokaryotic community composition and activities. Two complementary approaches, i.e., genomic and predictive metabolic analysis of the 16S rRNA V3–V4 amplicon and culture-dependent analysis, were combined to identify the main microbial factors that characterized the composting process. The whole microbial community was dominated by Firmicutes. The predictive analysis of the metabolic functionality of the community highlighted the potential degradation of peptidoglycan and the ability of metal chelation, with both functions being extremely useful for the revitalization and fertilization of agricultural soils. Finally, three biotechnologically relevant Firmicutes members, i.e., Geobacillus thermodenitrificans subsp. calidus, Aeribacillus pallidus, and Ureibacillus terrenus (strains CAF1, CAF2, and CAF5, respectively) were isolated from the “active thermophilic phase” of the coffee composting. All strains were thermophiles growing at the optimal temperature of 60 °C. Our findings contribute to the current knowledge on thermophilic composting microbiology and valorize burnt ground coffee as waste material with biotechnological potentialities.

Photoaging is the premature aging of the skin caused by repeated exposure to ultraviolet (UV) rays. The harmful effects of UV rays—from the sun or from artificial sources—alter normal skin structures and cause visible damage, especially in the most exposed areas. Fighting premature aging is one of the most important challenges of the medical landscape. Additionally, consumers are looking for care products that offer multiple benefits with reduced environmental and economic impact. The growing requests for bioactive compounds from aromatic plants for pharmaceutical and cosmetic applications have to find new sustainable methods to increase the effectiveness of new active formulations derived from eco-compatible technologies. The principle of sustainable practices and the circular economy favor the use of bioactive components derived from recycled biomass. The guidelines of the European Commission support the reuse of various types of organic biomass and organic waste, thus transforming waste management problems into economic opportunities. This review aims to elucidate the main mechanisms of photoaging and how these can be managed using natural renewable sources and specific bioactive derivatives, such as humic extracts from recycled organic biomass, as potential new actors in modern medicine.

We prepared humo-pectic hydrogels through ionotropic gelation by crosslinking natural pectins of different degree of methyl-esterification with either humic substances (HS) extracted from cow manure compost or humic-like substances (HULIS) from depolymerized lignocellulose biorefinery waste. The hydrogels were characterized by solid-state ¹³C-NMR spectroscopy, scanning electron microscopy, spectroscopic magnetic resonance imaging and rheological analyses. Their ability to work as controlled release systems was tested by following the release kinetics of a previously incorporated model phenolic compound, like phloroglucinol. Our results indicated that the release properties of hydrogels were influenced by the molecular composition of HS and HULIS and by the different degrees of methyl-esterification of pectins. The hydrogel made by the high methoxyl pectin and HS showed the fastest rate of phloroglucinol release, and this was attributed not only to its morphological structure and crosslinking density but also to the least formation of ionic interactions between phloroglucinol and the polysaccharidic chains. Our study suggests that the efficiency of novel humo-pectic hydrogels as sustainable carriers of agroproducts to crops is related to a careful choice of the characteristics of their components.

PurposeSOMPACS is a project recommended by EJP SOIL for funding under the 1st External Call "Towards Healthy, Resilient and Sustainable Agricultural Soils". The purpose of SOMPACS is to disclose management practices enriching soils with the organic matter pools that are most resistant to microbial decomposition. The project started in 2022 and will be implemented by a consortium of 12 research institutions from Poland, Germany, Ireland, Lithuania, UK, Italy and the USA until 2025.MethodsSoil samples from eight long-term field experiments with different soil management and cultivation systems (conventional tillage vs. no-tillage; mineral vs. organic fertilization; management with and without catch crop; arable land vs. grassland; and cultivated vs. non-cultivated soils) will be investigated. Field experiments will include trials of increasing duration: 22-year (Lithuania); 26-year (Italy); 30-year (Poland, Ireland); 46-year (Poland); 54-year (Lithuania); 100-year (Poland), and 178-year Broadbalk experiment (UK). Experiments will also be carried out in production fields, where additives that stimulate root growth and provide very stable C (commercial humic products, biochar, and biogas digestate) will be applied. The effects of these additives on the content and properties of SOM will be investigated also in experimental plots accompanied by the incubation studies on the microbial decomposition of SOM and these additives. In parallel with soil sampling, plant productivity will be measured in all field experiments. Basic soil properties will be supplemented by the following investigations based on state-of-the-art approaches: SOM composition and stability by Py-GC-MS; aggregate size classes and C pools of increasing physicochemical protection; analysis of δ13C and δ15N of the separated SOM pools; microbiological properties (community-level physiological profiling, selected functional genes involved in C and N cycles, microbiome and mycobiome analyzes by next-generation sequencing, genetic diversity using terminal restriction fragment length polymorphism); enzymatic activity; soil water retention and soil water repellency; mineral composition of clay fraction; soil structure stability. The most resistant SOM pool (humin) will be isolated by different methods (isolation vs. extraction) and examined for chemical composition and structure, using spectrometric and spectroscopic techniques (mass spectrometry, NMR, FTIR, EPR, UV-Vis-NIR, fluorescence). The C stocks in the soil profile will be evaluated and the extractable C in cold water will be determined to assess the potential leaching and microbial availability of C. Additionally, CO2 emissions from the soil of chosen experiments will be measured directly under field conditions.ResultsIn the first stage of the research, soil samples were collected from a depth up to 100 cm and the humin fraction from surface horizons was isolated for spectroscopic studies. Meantime, the impact of various types of cultivation on the yield was determined. ConclusionsA closer understanding of the persistence of SOC in top- and subsoil, as well as identifying management practices that contribute to minimizing greenhouse gas emissions, will show the possibilities of increasing the stable SOM pools, thus improving the potential of C sequestration. Understanding the impact of soil management on sustainable agricultural production and the environment, and in particular on climate change mitigation, should be widely promoted and put into practice.

Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (${\mathrm{R}}^2$ = 0.82; MAE = 6.36 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (${\mathrm{R}}^2$ = 0.86; MAE = 6.56 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons.